RL is a method to solve sequential decision making problems modeled as Markov Decision Process (MDP). Formally speaking, a discrete time finite horizon MDP is a tuple \((S,A,r,T,\gamma )\) , where \(S\)  is the state space, \(A\)  is the action space, \(r:S\times A\rightarrow r \in \mathbb {R}\)  is an instant reward value denoting the benefit of transition from current state \(s_t\in S\)  to the next state \(s_{t+1}\in S\) , and \(\gamma \)  is the discount factor [1]}. At each decision moment or discrete timestep \(t\) , an agent interacts with the environment and its goal is to learn a policy \(\pi : S \times A \rightarrow \pi \in [0,1]\)  that determines a probability value for each action. Following a greedy, \(\epsilon \) -greedy, softmax or other action selection policies, the agent takes an action according to the probabilities and transitions to the next state. In other words, the agent observes a state \(s_t\in S\)  and performs an action \(a_t \sim \pi (.|s_t)\) . Taking an action has two consequences. First the agent receives a reward value \(r_t\) . Then, the state of the environment transitions from \(s_t\)  to a new state \(s_{t+1}\)  based on the transition probability \(T\) . The agent updates the policy during the learning phase in order to find the optimal policy that yields the maximum total reward. RL provides an interaction-based framework to solve the MDP and learn the policy \(\pi \) .
